Unintentional falls account for greater than 30,000 annual deaths within the US population. Seniors are most vulnerable to falling and, as a result, suffer more than 300,000 hip fractures a year. Of those who fracture a hip, 50% will never return to their homes. The poor balance that contributes to these fall events often declines for decades in advance of the fall event, yet the conventional method for tackling poor balance is to seek medical diagnostics and interventions only after a fall has occurred or the patient has a very serious balance problem. In fact, the current best predictor of a fall is whether someone has already fallen.
To truly improve the statistics of falls across the country, preventive intervention should be performed in advance of the first fall. Balance is similar to other physical performances, it can be improved with practice and, conversely, deteriorates with disuse. A number of lifestyle and health factors are known to influence one's balance, such as exercise, strength, sleep, cognitive functioning, vitamin D supplements, and medication management. Lifestyle changes to improve balance will take time to build up their protective effect. Measuring balance and fall risk affords the opportunity to detect subtle balance changes that can occur with health and lifestyle adjustments.
The human balance control system is very complex with three or more sensory inputs creating a repertoire of motor outputs, each with differing strategies that are affected by subconscious and conscious control, experience, context, and personality. The circumstances surrounding falling further complicates matters as the source of a fall can be from numerous intrinsic and extrinsic factors. Consequently, predicting falls with a basic measure of balance is insufficient on its own. The added insight and predictive power that machine learning techniques provide for human balance control systems can facilitate a more accurate prediction of falls.
One such machine learning approach is discussed in U.S. Pat. No. 8,011,229. The '229 patent uses Hidden Markov Model techniques for determining postural stability by identifying different postural states from center of pressure (COP) data. COP is the central location of combined pressure from 2 or more pressure or load sensors. The postural states relate to a classification of either static or dynamic. As the names suggest, a static postural state is defined as a dwell region within the COP data wherein sway is constrained to a single equilibrium. While a person is in a static state their body sway is considered under control and the person is more balanced and less likely to fall. A dynamic postural state is defined as sections of COP data that are not constrained to any equilibria and are by definition, unconstrained or uncontrolled. While a person is in a dynamic state they are considered to be “escaping” an equilibrium and are either moving to another equilibrium or falling. The static and dynamic postural states facilitate an assessment of postural stability undocumented before, defining a new model of postural control: the punctuated equilibrium model (PEM). The PEM is defined as periods of stability punctuated by dynamic trajectories. The PEM classification of postural states is particularly applicable for real-time or near-real-time assessment of stability. However, subsequent metrics that quantify the postural states facilitate a determination of instability trends along longer timelines. Measures of postural instability within the PEM are identified as: number of equilibria, equilibria dwell time and size of equilibria.
There are a number of advantages of the PEM approach. Firstly, the technique classifies otherwise uniform data, identifying stable regions and dynamic trajectories, with the latter being viewed as unstable. Threshold functions are described to identify the postural state users are in, whether for real-time identification or long term detection of postural instability. Further, the approach creates relative measures of stability that create independence from height and weight, location of the feet, or known stability boundaries.
While the preceding approach improved insights into postural stability, it is commonly understood that the multi-factorial nature of falls means that predicting falls outside of the real-time and near-real-time fall range is difficult to achieve. Despite the development to date, there remains a need for improved postural stability representation.